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For sign languages, transitional movements of the hands are fully visible and may be used to predict upcoming linguistic input. We investigated whether and how deaf signers and hearing nonsigners use transitional information to detect a target item in a string of either pseudosigns or grooming gestures, as well as whether motor imagery ability was related to this skill. Transitional information between items was either intact (Normal videos), digitally altered such that the hands were selectively blurred (Blurred videos), or edited to only show the frame prior to the transition which was frozen for the entire transition period, removing all transitional information (Static videos). For both pseudosigns and gestures, signers and nonsigners had faster target detection times for Blurred than Static videos, indicating similar use of movement transition cues. For linguistic stimuli (pseudosigns), only signers made use of transitional handshape information, as evidenced by faster target detection times for Normal than Blurred videos. This result indicates that signers can use their linguistic knowledge to interpret transitional handshapes to predict the upcoming signal. Signers and nonsigners did not differ in motor imagery abilities, but only non-signers exhibited evidence of using motor imagery as a prediction strategy. Overall, these results suggest that signers use transitional movement and handshape cues to facilitate sign recognition.

Gesture detection has recently attracted a lot of attention due to its wide range of applications, notably in human–computer interaction (HCI). However, when it comes to video-based gesture recognition, elements in the background unrelated to gestures slow down the system's classification rate. This paper presents an algorithm designed for the recognition of large-scale gestures. In the training phase, we utilize RGB-D videos, where the depth modality videos are derived from RGB modality videos using UNET and subsequently employed for testing. However, it's worth noting that in real-time applications of the proposed dynamic hand gesture recognition (DHGR) system, only RGB modality videos are needed. The algorithm begins by creating two dynamic images: one from the estimated depth video and the other from the RGB video. Dynamic images generated from RGB video excel in capturing spatial information; while, those derived from depth video excel in encoding temporal aspects. These two dynamic images are merged to form an RGB-D dynamic image (RDDI). The RDDI is then fed into a modified Xception-based CNN model for the purpose of gesture classification and recognition. In order to evaluate the system's performance, we conducted experiments using the EgoGesture and MSR Gesture datasets. The results are highly promising, with a reported classification accuracy of 91.64% for the EgoGesture dataset and an impressive 99.41% for the MSR Gesture dataset. The results demonstrated that the suggested system outperformed some existing techniques. [ABSTRACT FROM AUTHOR]

Nowadays, artificial intelligence-assisted automatic technological analyses of sports matches have received a more general demand. Among them, the effective recognition of sports actions from videos or images acts as the most fundamental issue to be solved. As sports gestures are characterized by remarkable specialty and instantaneity, it is required to develop specific effective recognition algorithms for this purpose. As a result, this work takes the case of Taekwondo sports as an example, and introduces deep vision learning to develop a specific intelligent recognition method for dynamic sports gestures. After sampling the key frames from dynamic videos, vital joint points of limbs are first obtained as the basic bottom features. Then, gesture expertise of this domain is formed to construct a multiview feature extraction work flow to better analyze complex characteristics of each gesture. Finally, the extracted feature representation is input into a recurrent neural network structure to output the discriminative results. At last, experiments are conducted for assessment, whose results show that the proposal can reach an accuracy of 95.6% and can be well suitable for the investigated problem scenario. [ABSTRACT FROM AUTHOR]

Nowadays, typing is considered as one of the highly important aspects of accessibility that the visually impaired and blinds struggle with the most as existing virtual keyboards are complex and slow. This paper proposes a new text entry method named SwingBoard for visually impaired and blind smartphone users to solve their accessibility problem. It supports a–z, 0–9, 7 punctuations, 12 symbols, and eight keyboard functionalities that are arranged in 8 zones (specific range of angles), four segments, two modes, and different gestures. The proposed keyboard is suitable for the either single-handed or both-handed operation that tracks swipe angle and length to trigger any of the 66 key events. The key triggering process is based on only swiping the finger at different angles with different lengths. Typing speed of SwingBoard is increased by including some effective features such as the quick alphabet and number mode shifting, haptic feedback feature, talkback on swipe to learn the map quickly, and customizable swipe length feature. At the end of 150 one-minute tests, seven blind participants reached an average of 19.89 words per minute (WPM) with an 88% accuracy rate which is one of the fastest-ever recorded average typing speeds for the blind. Almost all users found SwingBoard effective, easy to learn and want to keep using it. SwingBoard is a handy virtual keyboard for visually impaired people with amazing typing speed and accuracy. Rehabilitation would be easier for a visually impaired/disabled person who can adapt the current technology-oriented world and use communication tools easily. As visually impaired and blind people faced a lot of problems in their regular life because of their inability to see things, coping with the evolving world's demands from a person to do a certain task using smart devices would be easier for them with the proposed virtual keyboard topology. As the number of visually impaired and blind users of smartphones rises, faster typing becomes a vital aspect of the smartphone experience. Deaf-blind communities are expanding but solutions for them are not increasing at the same pace because of the limitation of implication. Research on a virtual keyboard with the proposed eyes-free swipe-based typing operation and ears-free reliability on haptic feedback would enable others to create new solutions. [ABSTRACT FROM AUTHOR]

People are more disfluent in their second language (L2) than their first language (L1). Gesturing facilitates cognitive processes, including speech production. This study investigates speech disfluency and representational gesture production across Turkish-English bilinguals’ L1 (Turkish) and L2 (English) through a narrative retelling task (N = 27). Results showed that people were more disfluent and used more representational gestures in English. Controlling for L2 proficiency, people were still more disfluent in English. The more people were proficient in L2, the more they used gestures in that language. Similarly, disfluency-gesture co-occurrences were more common in English. L2 proficiency was positively correlated with the likelihood of a disfluency being accompanied by a gesture. These findings suggest that gestures may not necessarily compensate for weak language skills. Rather, people might gesture during disfluent moments if they can detect their errors, suggesting a close link between representational gestures and language competency in benefiting from gestures when disfluent. [ABSTRACT FROM AUTHOR]

Intentional Teaching Gestures are gestures that have been created as a pedagogical tool for second language teaching and learning and are used in many Japanese programs. Despite their widespread use, little is known about their impact on students' oral language production. Therefore, the purpose of this study was to explore the effect of Intentional Teaching Gestures on students' Japanese production. This paper reports on the quality of students' oral language production from two Story Re-tell tasks, one with and one without viewing Intentional Teaching Gestures (ITG), after learning Japanese as a second language with ITG. Thematic analysis was undertaken of the Story Re-tell transcription data from 170 primary school participants. Findings identify that viewing ITG whilst doing Story Re-tell positively impacted learners' oral production. The breadth of benefits included increases in the quantity of output that students produced, the expanded informational content included in their stories, and students' increased use of a range of verbs and particles in their utterances. Viewing ITG also provided support for students' structuring of utterances, supported self correction, and led to an improvement in their overall oral performance. These benefits have wide ranging pedagogical implications for Japanese teachers and learners. [ABSTRACT FROM AUTHOR]

Communication with hand gestures plays a significant role in human‐computer interaction by providing an intuitive and natural way for humans to communicate with machines. Ultrasound‐based devices have shown promising results in contactless hand gesture recognition without requiring physical contact. However, it is challenging to fabricate a densely packed wearable ultrasound array. Here, a stretchable ultrasound array is demonstrated with closely packed transducer elements fabricated using surface charge engineering between pre‐charged 1–3 Lead Zirconate Titanate (PZT) composite and thin polyimide film without using a microscope. The array exhibits excellent ultrasound properties with a wide bandwidth (≈57.1%) and high electromechanical coefficient (≈0.75). The ultrasound array can decipher gestures up to 10 cm in distance by using a contactless triboelectric module and identify materials from the time constant of the exponentially decaying impedance based on their triboelectric properties by utilizing the electrostatic induction phase. The newly proposed metric of the areal‐time constant is material‐specific and decreases monotonically from a highly positive human body (1.13 m2 s) to negatively charged polydimethylsiloxane (PDMS) (0.02 m2 s) in the triboelectric series. The capability of the closely packed ultrasound array to detect material along with hand gesture interpretation provides an additional dimension in the next‐generation human‐robot interaction. [ABSTRACT FROM AUTHOR]

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher's gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher's skeleton and then inputs the recognized sequence of the teacher's skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed. [ABSTRACT FROM AUTHOR]

Sketching is a common way to depict design ideas freely. Augmented reality technology extends the human–computer interaction space from the screen to the real world, providing a new application space for 3D sketching. However, due to the difficulty of depth perception, it is difficult for sketching strokes to maintain good planar and spatial consistency, making drawing 3D sketches in AR challenging. In this article, we propose FMASketch—a method of drawing mid-air sketches similar to building blocks. The auxiliary surfaces can ensure that the strokes drawn by users can always stay on the same plane as drawing in 2D space. Gestures are used to stitch together multiple drawing planes, and the strokes on the planes form a 3D sketch, which is very similar to how 3D modeling is done on the desktop by drawing different views and assembling them. Experiments have verified the effectiveness of the auxiliary surface generation method and gesture operations in this article. The mid-air sketching method is easy to learn and provides a new idea for rapid modeling in AR scenarios. [ABSTRACT FROM AUTHOR]

When learning spoken second language (L2), words overlapping in form and meaning with one's native language (L1) help break into the new language. When nonsigning speakers learn a sign language as L2, such overlaps are absent because of the modality differences (L1: speech, L2: sign). In such cases, nonsigning speakers might use iconic form‐meaning mappings in signs or their own gestural experience as gateways into the to‐be‐acquired sign language. In this study, we investigated how both these phenomena may contribute jointly to the acquisition of sign language vocabulary by hearing nonsigners. Participants were presented with three types of signs in the Sign Language of the Netherlands (NGT): arbitrary signs, iconic signs with high or low gesture overlap. Signs that were both iconic and highly overlapping with gestures boosted learning most at first exposure, and this effect remained the day after. Findings highlight the influence of modality‐specific attributes supporting the acquisition of a signed lexicon. [ABSTRACT FROM AUTHOR]

Background Aims Methods and procedures Outcomes and results Conclusions and implications What this paper adds What is already known on the subject What this paper adds to existing knowledge What are the potential or actual clinical implications of this work? In typically developing (TD) children, gesture emerges around 9 months of age, allowing children to communicate prior to speech. Due to the important role gesture plays in the early communication of autistic and TD children, various tasks have been used to assess gesture ability. However, few data exist on whether and how tasks differentially elicit gesture, particularly for samples of racially and ethnically diverse autistic children.In this study, we explored if task (a naturalistic parent–child interaction [NPCI]; structured assessment of child communication) differentially elicited rate or type of gesture production for young autistic children.This secondary analysis included baseline data from 80 racially and ethnically diverse autistic children aged 18–59 months who participated in one of two larger studies. Video recordings of NPCIs and an assessment of child communication with standardised administration procedures were collected at baseline. Child gesture rate (number of gestures produced per 10 min) and type were extracted from these recordings and analysed.The structured assessment elicited more gestures than the NPCI. In terms of gesture type, points, gives, and reaches accounted for 76% of child gestures. Points (which are developmentally more advanced than reaches and gives) were produced at the highest rates within book exploration. Distal points (which are more developmentally advanced than proximal or contact points) were produced at the highest rates when children were tempted to request.Our findings indicate elicitation tasks differentially elicit type and rate of gesture for young autistic children. To assess the gesture production of young autistic children, a structured task designed to elicit child requests will probe the developmental sophistication of the child's gesture repertoire, eliciting both the most gestures and the most developmentally advanced gestures. Because of the importance of gesture in early communication for autistic and typically developing children, various tasks have been used to assess it. However, little is known about whether tasks differentially elicit type or rate of gesture for young autistic children from diverse racial and ethnic backgrounds. Elicitation tasks differentially elicit type and rate of gesture for young autistic children in the early stages of gesture. We recommend a structured task designed to elicit child requests to assess the developmental sophistication of a child's gesture repertoire. [ABSTRACT FROM AUTHOR]

Using pose estimation with video recordings, we apply an action recognition machine learning algorithm to demonstrate the use of the movement information to classify singers and the ragas (melodic modes) they perform. Movement information is derived from a specially recorded video dataset of solo Hindustani (North Indian) raga recordings by three professional singers each performing the same nine ragas, a smaller duo dataset (one singer with tabla accompaniment) as well as recordings of concert performances by the same singers. Data is extracted using pose estimation algorithms, both 2D (OpenPose) and 3D. A two-pathway convolutional neural network structure is proposed for skeleton action recognition to train a model to classify 12-second clips by singer and raga. The model is capable of distinguishing the three singers on the basis of movement information alone. For each singer, it is capable of distinguishing between the nine ragas with a mean accuracy of 38.2% (with the most successful model). The model trained on solo recordings also proved effective at classifying duo and concert recordings. These findings are consistent with the view that while the gesturing of Indian singers is idiosyncratic, it remains tightly linked to patterns of melodic movement: indeed we show that in some cases different ragas are distinguishable on the basis of movement information alone. A series of technical challenges are identified and addressed, with code shared alongside audiovisual data to accompany the paper. [ABSTRACT FROM AUTHOR]

Pseudoreplication is the statistical error of collecting numerous datapoints from a single unit (such as an individual), which are not independent, and applying statistical methods that assume independence of data. Importantly, pseudoreplication increases the chances of Type 1 errors (i.e., false positives), bringing findings and conclusions based on pseudoreplicated analyses into question. Ten years ago, Waller et al. (2013) published a paper highlighting the prevalence of statistical pseudoreplication throughout the nonhuman primate communication literature. In this current study, we examined the literature published since the original publication (between 2009 and 2020; 348 papers) to assess whether pseudoreplication is still as widespread as it was, if it has become more problematic, or if the field is beginning to overcome this issue. We find that there has been a significant decrease in pseudoreplication over the past ten years (38.6% then, compared with 23.0% now). This reduction in pseudoreplication appears to be associated with an increase in the use of multilevel models throughout primatology (which allow for nonindependent data to be nested appropriately). Pseudoreplication was historically more prevalent in research using observational (vs. experimental) methods and those working with wild (vs. captive) primates. However, these biases do not seem to exist in more recent literature with a more comparable likelihood of pseudoreplication seen across the field regardless of methods. Although these current findings relate specifically to primate communication research, we think they will translate broadly across nonhuman communication research, and throughout biology. We continue to emphasise the need to monitor these issues, as although now seen at much lower rates, pseudoreplication is still present and therefore potentially impacting the accuracy of findings. [ABSTRACT FROM AUTHOR]

This article introduces a novel approach for building a robust Automatic Gesture Recognition system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level. Our main contribution is to propose new constrained learning strategies that ensure robustness against adversarial perturbations by controlling the Lipschitz constant of the classifier. We focus on nonnegative neural networks for which accurate Lipschitz bounds can be derived, and we propose different spectral norm constraints offering robustness guarantees from a theoretical viewpoint. Experimental results on four publicly available datasets highlight that a good tradeoff in terms of accuracy and performance is achieved. We then demonstrate the robustness of our models, compared with standard trained classifiers in four scenarios, considering both white-box and black-box attacks. [ABSTRACT FROM AUTHOR]

Mediated social touch (MST) is a popular way to communicate emotion and connect people in mobile communication. This article applies MST gestures with vibrotactile stimuli in two online communication modes—asynchronous and synchronous communication (texting and video calling) to enhance social presence for mobile communication. We first designed the application that included the visual design of MST gestures, the vibrotactile stimuli design for MST gestures, and the interface design for texting and video calling. Then, we conducted a user study to explore if the MST gestures with vibrotactile stimuli could increase social presence in texting and video calling compared to MST gestures without vibrotactile stimuli. We also explored if the communication modes affected the social presence significantly when applying MST signals. The quantitative data analysis shows that adding vibrotactile stimuli to MST gestures helps to increase social presence in the aspects of co-presence, perceived behavior interdependence, perceived affective understanding, and perceived emotional interdependence. Adding vibrotactile stimuli to MST gestures causes no significant differences in attentional allocation and perceived message understanding. There is no significant difference between texting and video calling when applying MST signals in mobile communication. The qualitative data analysis shows that participants think MST gestures with vibrotactile stimuli are interesting, and they are willing to use them in mobile communication, but the application design should be iterated based on their feedback. [ABSTRACT FROM AUTHOR]

What remains of a national day after it has passed? Weeks after it has ended banners celebrating China's 'birthday' on October first can still be seen all over Hong Kong. As government and pro‐China organizations hang up banners and flags all around Hong Kong, celebrating the birthday of the nation. These flags remind the people in Hong Kong that they are part of China, despite some thinking otherwise. I argue that these banners serve to 1. Remind people of Hong Kong's subordinate position within the nation state of China, 2. Show how the nation is performed, and 3. force the nation onto people as a forceful rather than a banal gesture. Drawing on theories of affect and space, ethnography, and photos taken by the author, this study adds to the theory and process of everyday nation‐making. [ABSTRACT FROM AUTHOR]

This paper focuses on deep vision sensing-assisted gesture recognition for athletes in dynamic scenes. Although many research attention had been devoted to this field in recent years, most of existing works failed to fully take characteristics of dynamic scenes into consideration. To deal with this challenge, this paper proposes a diffusion convolution neural network-based multiview gesture recognition approach in dynamic scenes. For one thing, the dynamic spatiotemporal slice position selection based on the body mask heatmap is adopted to calculate positions of horizontal and vertical slices. Thus, the dynamic selection of slice positions in two directions can be realized, and then the extraction of bi-directional spatiotemporal slice images can be completed. For another, action sequences through the 3D residual neural network are learned, and the spatiotemporal information among frames are mined through recurrent networks. Through their combination, a multi-view gesture recognition approach for athletes is constructed. In the experiments, two standard datasets UCF101 and HMDB51 are utilized to establish simulation environment. The proposed method can reach the accuracy beyond 95% on the two datasets. Compared with several typical recognition methods, the proposed method shows higher accuracy. [ABSTRACT FROM AUTHOR]

This article explores questions of positionality in relation to the gaze towards migrants; it is centred on an analysis of three films produced in Europe in the wake of public discussions on migration. It argues that each of these films shows an affinity with one of three possible theoretical approaches in migration research: Fuocoammare is interpreted as corresponding to the mainstream scientific gaze and the humanitarian gesture; Les Sauteurs is considered in relation to the 'autonomy of migration' paradigm followed in much critical migration research; and Those who feel the fire burning is analysed through the haunting paradigm or 'spectral turn' that has been incipiently explored with regards to migration. I develop parallels between the films' conceptual and visual positioning and these research approaches, formulating questions of relationality towards migrants before European audiences. Rather than presenting an analysis of the films for their formal or aesthetic qualities, they are taken as a point of entry to reflect on our own positionality and choices as scholars. [ABSTRACT FROM AUTHOR]

This article explores the literary, philosophical, and phenomenological dimensions of neighbor‐love. Phenomenologically speaking, neighborly love must be given, that is, it must be given voluntarily through attitudes, actions, or gestures. But whom do we actually acknowledge as our neighbor, and why? Adopting a comparative literary approach, this paper argues that literature is not philosophy's adversary but its creative interlocutor: Ilse Aichinger's poem "Foundling" transcends anthropocentric perspectives, presenting the Neighbor as a being beyond denomination by translating it from human to animal. Doris Lessing's novel The Diary of a Good Neighbour depicts the unpredictable and accidental nature of encounters with the Neighbor, leaving no room for personal choice. Ultimately, Amélie Nothomb's Les catilinaires illustrates how the Neighbor can be a persistent annoyance that both irritates and resists systematic thinking. These literary works outline a nuanced poetics of neighbor‐love and givenness that extends beyond any anthropological, theological, or religio‐ethical concept. [ABSTRACT FROM AUTHOR]

Swearing has been shown to reduce the experience of pain in a cold pressor task, and the effect has been suggested to be due to state aggression. In the present experiment, we examined whether producing a taboo gesture (i.e., the American gesture of raising the middle finger) reduces the experience of pain similar to the effect that has been shown for producing a taboo word. 111 participants completed two cold pressor trials in a 2 (Language vs. Gesture) × 2 (Taboo vs. Neutral) mixed design. We found that producing a taboo act in either language or gesture increased pain tolerance on the cold pressor task and reduced the experience of perceived pain compared to producing a neutral act. We found no changes in state aggression or heart rate. These results suggest that the pain-reducing effect of swearing is shared by taboo gesture and that these effects are likely not due to changes in state aggression. [ABSTRACT FROM AUTHOR]

An anecdote, ascribed to Dicaearchus by Athenaeus (13.603.a–b) and found also in Plutarch (Alexander 67.7–8), details Alexander's kissing of the eunuch Bagoas during theatrical contests in Carmania in 325 bce. This article examines the enthusiastic response that this encounter is said to have elicited from Alexander's soldiers. It is suggested that this impromptu kiss was read as a display of homoerotic behaviour that was fundamentally Greek, and even as a gesture of Greek domination over Persians, and that, as such, it was welcomed by the army as a momentary departure from the increasingly Persianized behaviour of the king himself and of his court. Further, consideration is given to the possibility that the choral contest in which Bagoas is reported to have competed prior to the kiss may have been an innovative form of pyrrhic dance, in which for the first time the dancers depicted Dionysus' conquest of India. [ABSTRACT FROM AUTHOR]

Modern materials with unique structures and functions are inspired by nature. However, nature's craftsmanship is beyond the reach of traditional handmade techniques. 3D printing technology creates the possibility of creating bionic structures. In this study, inspired by the ability of spiders to sense the capture of prey through the vibration of their webs, a spider‐web‐like, highly sensitive hydrogel sensor is designed and printed. The hydrogel is made of eco‐friendly green polyvinyl alcohol cross‐linked with microcrystalline cellulose. Surprisingly, the 3D‐printed hydrogel sensor can manipulate the sensitivity by changing the angle and strain range of the structure. Accordingly, the Gauge factor (GF) of the 3D‐printed hydrogel (θ = 60°) is up to 58.23, which is 23.9 times higher than that of the non‐3D‐printed hydrogel sensor (GF = 2.43). The GF of the 3D‐printed hydrogel at other angles still remained high at 24.74 (θ = 90°) and 12.31 (θ = 120°), respectively. It can be seen that the 3D‐printed hydrogel sensor has advanced performance. In addition, an intelligent gesture recognition system for human–computer interaction is constructed using 3D‐printed hydrogel sensors, which offers potential prospects for helping people communicate. [ABSTRACT FROM AUTHOR]

BackgroundAimsMethods & ProceduresOutcomes & ResultsConclusionsPrimary progressive aphasia (PPA) is a neurodegenerative syndrome characterized by progressive language deficits. The main variants of PPA – semantic (svPPA), logopenic (lvPPA), and nonfluent (nfvPPA) – can be challenging to distinguish. Limb apraxia often co-occurs with PPA, but it is unclear whether PPA variants are associated with different gesture deficits. Prior evidence from stroke indicates that temporal lobe lesions are associated with reduced benefit from meaning in gesture performance. Temporal lesions are also associated with greater deficits in hand postures compared to arm kinematics in gesture imitation.We tested the hypothesis that svPPA, who primarily exhibit temporal lobe atrophy, would differentially show this pattern. In addition, we predicted that as a group, individuals with PPA would more accurately produce meaningful than meaningless gestures and kinematic components than hand postures, as found in neurotypical adults and individuals with stroke.Participants with PPA completed meaningful and meaningless gesture imitation tasks. Performance was scored for hand posture and arm kinematic components. Bayesian mixed-effect models examined trial-level gesture imitation accuracy, while controlling for dementia severity and random effects of subjects and items. Fixed effects included an interaction between PPA variant, task condition/meaning, and gesture component.We found a critical interaction between PPA variant, meaning, and gesture component. In particular, unlike lvPPA and nfvPPA, svPPA subjects’ hand postures failed to benefit from gesture meaning. This preliminary research extends prior findings on the role of the temporal lobe in action representations associated with manipulable objects, and is among the first to demonstrate distinct gesture imitation patterns between PPA variants.Characterizing gesture deficits in PPA may help inform diagnostics, compensatory communication strategies, and models of praxis. [ABSTRACT FROM AUTHOR]

This paper proposes a new interpretation of Gadamer's problematic appropriation of Platonic metaphysics. It argues that Gadamer, attempting to respond to the challenge posed by Heidegger's interpretation of Platonic metaphysics and of its role in the history of Being (Seinsgeschichte), downplayed the transcendence of Platonic Forms. Gadamer achieves a reconfiguration of this transcendence and its transposition into what I call here a plane of immanence through two hermeneutic gestures: 1) interpreting Forms in light of Greek mathematics and especially in light of the structure of the sum-number; 2) introducing temporality and historicity in the Medieval doctrine of transcendentals by giving priority to the Beautiful over the Good. I contend that, Platonically speaking, this amounts to the rejection of νοήσις in favor of διάνοια, and that this raises issues concerning the problem of finitude and the potential limits of linguisticality (Sprachlichkeit). [ABSTRACT FROM AUTHOR]

Purpose: Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition. Design/methodology/approach: A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network. Findings: The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures. Research limitations/implications: The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures. Practical implications: A new approach to gesture recognition using wearable devices. Social implications: This study has a broad application prospect in the field of human–computer interaction. Originality/value: The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction. [ABSTRACT FROM AUTHOR]

Public speakers like politicians carefully craft their words to maximize the clarity, impact, and persuasiveness of their messages. However, these messages can be shaped by more than words. Gestures play an important role in how spoken arguments are perceived, conceptualized, and remembered by audiences. Studies of political speech have explored the ways spoken arguments are used to persuade audiences and cue applause. Studies of politicians' gestures have explored the ways politicians illustrate different concepts with their hands, but have not focused on gesture's potential as a tool of persuasion. Our paper combines these traditions to ask first, how politicians gesture when using spoken rhetorical devices aimed at persuading audiences, and second, whether these gestures influence the ways their arguments are perceived. Study 1 examined two rhetorical devices—contrasts and lists—used by three politicians during U.S. presidential debates and asked whether the gestures produced during contrasts and lists differ. Gestures produced during contrasts were more likely to involve changes in hand location, and gestures produced during lists were more likely to involve changes in trajectory. Study 2 used footage from the same debates in an experiment to ask whether gesture influenced the way people perceived the politicians' arguments. When participants had access to gestural information, they perceived contrasted items as more different from one another and listed items as more similar to one another than they did when they only had access to speech. This was true even when participants had access to only gesture (in muted videos). We conclude that gesture is effective at communicating concepts of similarity and difference and that politicians (and likely other speakers) take advantage of gesture's persuasive potential. [ABSTRACT FROM AUTHOR]

Temporal perspectives allow us to place ourselves and temporal events on a timeline, making it easier to conceptualize time. This study investigates how we take different temporal perspectives in our temporal gestures. We asked participants (n = 36) to retell temporal scenarios written in the Moving‐Ego, Moving‐Time, and Time‐Reference‐Point perspectives in spontaneous and encouraged gesture conditions. Participants took temporal perspectives mostly in similar ways regardless of the gesture condition. Perspective comparisons showed that temporal gestures of our participants resonated better with the Ego‐ (i.e., Moving‐Ego and Moving‐Time) versus Time‐Reference‐Point distinction instead of the classical Moving‐Ego versus Moving‐Time contrast. Specifically, participants mostly produced more Moving‐Ego and Time‐Reference‐Point gestures for the corresponding scenarios and speech; however, the Moving‐Time perspective was not adopted more than the others in any condition. Similarly, the Moving‐Time gestures did not favor an axis over the others, whereas Moving‐Ego gestures were mostly sagittal and Time‐Reference‐Point gestures were mostly lateral. These findings suggest that we incorporate temporal perspectives into our temporal gestures to a considerable extent; however, the classical Moving‐Ego and Moving‐Time classification may not hold for temporal gestures. [ABSTRACT FROM AUTHOR]

This research uses a real-time, human-computer interaction application to examine sign language recognition. This work develops a rule-based hand gesture approach for Indonesian sign language in order to interpret some words using a combination of hand movements, mimics, and poses. The main objective in this study is the recognition of sign language that is based on hand movements made in front of the body with one or two hands, movements which may involve switching between the left and right hand or may be combined with mimics and poses. To overcome this problem, a research framework is developed by coordinating hand gestures with poses and mimics to create features by using holistic MediaPipe. To train and test data in real time, the long short time memory (LSTM) and gated recurrent unit (GRU) approaches are used. The research findings presented in this paper show that hand gestures in real-time interactions are reliably recognized, and some words are interpreted with the high accuracy rates of 94% and 96% for the LSTM and GRU methods, respectively. [ABSTRACT FROM AUTHOR]

Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k-nearest neighbors (k-NN) with Euclidean distance, k-NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences. [ABSTRACT FROM AUTHOR]

Nonverbal communication impacts a broad range of human interactions and bears significant influence on understanding and perceptions. One of the most influential categories of nonverbal communication is kinesics, which includes physical gestures and other body movements. The current study develops a foundation for future kinesics education in sales by examining the use of four types of gestures (illustrators, beats, adaptors, and regulators) and coinciding measures of sales performance within the context of a national student sales competition. Results include the finding that highly rated sales students use one specific kind of gesture – illustrators - more frequently than low performing sales students. No significant relationships were uncovered between beats, adaptors, or regulators and performance. Educational implications and recommendations for sales courses are provided. [ABSTRACT FROM AUTHOR]

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13 % and 86.76 % for 1 and 3 data samples. [ABSTRACT FROM AUTHOR]

Just as vocalization proceeds in a continuous stream in speech, so too do movements of the hands, face, and body in sign languages. Here, we use motion-capture technology to distinguish lexical signs in sign language from other common types of expression in the signing stream. One type of expression is constructed action, the enactment of (aspects of) referents and events by (parts of) the body. Another is classifier constructions, the manual representation of analogue and gradient motions and locations simultaneously with specified referent morphemes. The term signing is commonly used for all of these, but we show that not all visual signals in sign languages are of the same type. In this study of Israeli Sign Language, we use motion capture to show that the motion of lexical signs differs significantly along several kinematic parameters from that of the two other modes of expression: constructed action and the classifier forms. In so doing, we show how motion-capture technology can help to define the universal linguistic category "word," and to distinguish it from the expressive gestural elements that are commonly found across sign languages. [ABSTRACT FROM AUTHOR]

This study aimed to better understand how autism spectrum disorder (ASD) and developmental coordination disorder (DCD) differ in types of praxis errors made on the Florida Apraxia Battery Modified (FAB-M) and the potential relationships between praxis errors and social deficits in ASD. The ASD group made significantly more timing sequencing errors in imitation of meaningful gestures, as well as more body-part-for-tool errors during gesture-to-command compared to the other two groups. In the ASD group, increased temporal errors in meaningful imitation were significantly correlated with poorer affect recognition and less repetitive behaviors. Thus, in ASD, aspects of imitation ability are related to socioemotional skills and repetitive behaviors. [ABSTRACT FROM AUTHOR]

The acquisition of communicative competence in second and foreign languages requires the incorporation of verbal and non-verbal elements. Notwithstanding, few studies have performed empirical research into the acquisition of non-verbal signs. This research studies the learning of emblematic gestures for students of Spanish in the USA using an evolutionary analysis after instruction. Interactions between types of gestures (common, different, and unique) based on the similarities with the first language and learning mechanisms in free and guided comprehension and production tasks are taken into account. The results indicate that although the detection and production of emblems improve with instruction, the progress is unequal: the categories different and unique obtain a higher rate of improvement than common emblems in specific tasks. In conclusion, it is essential for the teaching of gestures and non-verbal communication to consider, along with the non-verbal target language code, the non-verbal mother tongue code. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods. [ABSTRACT FROM AUTHOR]

Speakers with aphasia gesture, but the extent to which these gestures improve their communication success is unclear. The primary aim is to assess if gesture improves the communication success of people with aphasia using a systematic review. Following the PRISMA protocol, we systematically reviewed the literature assessing the contribution of gesture to the communication success of people with aphasia. Multiple electronic databases were searched using specified keywords and MeSH explode. This identified 2177 articles, seven of which met our inclusion criteria. The included articles were reviewed in the context of three research questions, which concluded that: (1) the communication success of gesture is compromised in people with aphasia when compared to healthy language users, (2) gesture improves the communication success of people with aphasia beyond spoken language alone, and 3) apraxia severity impedes the communication success of gesture, whereas aphasia severity and semantic processing deficits do not. This systematic review clarifies inconsistencies in the literature and confirms that gesture can improve the communication success of people with aphasia. This supports the continued use of gesture as a therapeutic intervention for people with aphasia. [ABSTRACT FROM AUTHOR]